The etiology of Alzheimer's Disease (AD), now the third most costly medical condition in the U.S., is likely influenced by diet. Several nutrients and or foods have been hypothesized to reduce the risk of age-related cognitive decline and AD but analyses of individual dietary components largely ignore the complexity of diet. Naturally, nutrients are consumed in foods and foods are consumed together in dietary patterns. Characterizing and analyzing food consumption as a dietary pattern may better predict disease risk and provide a clearer link to food-based public health recommendations. The general goals of this project are to identify patterns of dietary intake among a large cohort of elderly men and women;and to use the identified dietary patterns to elucidate the role of total diet in cognitive decline and AD. We hypothesize dietary patterns, rather than intakes of single foods or nutrients, will better represent the influence of total diet on risk for cognitive decline and AD in late life. Lifestyle recommendations that target modifications to total diet may be a powerful and practical prevention strategy for this devastating disease that affects millions of elders worldwide. We propose the following three Specific Aims using resource of The Cache County Memory Study (CCMS) a prospective cohort study with exceptionally long-lived individuals, high participation rates, and careful clinical characterization of cognitive function: (1) Characterize dietary patterns using repeated measurements of diet and multiple a priori and posteriori methods, including the novel use of archetypal analysis;(2) Examine relationships between dietary patterns and biological markers of metabolic dysfunction including hemoglobin A1C, CRP, red blood cell fatty acid distribution and fasted levels of insulin and glucose;and 3) Evaluate the prospective associations between dietary patterns identified in SA1 and cognitive outcomes including cognitive decline, risk of AD, and age of dementia onset. In particular, the use of Random Forests, a novel tree-based method used for classification and regression of high-dimensional data sets such as that characterizing diet will be explored. PUBLIC HEALTH RELEVANCE: Cognitive decline is a risk factor of Alzheimer's Disease (AD), the third most costly medication condition in the U.S. today. We propose to study associations between dietary patterns, biological markers of insulin resistance and inflammation, and rates of cognitive decline and risk of AD using previously collected specimens and clinical data from the Cache County Memory Study in Utah.